Abstract
The personalized recommendation system influences the recommendation of ideological and political teaching resources in universities, resulting in a high MAE score. As a result, under the school-enterprise collaboration paradigm, this study proposes a customised recommendation approach for ideological and political teaching resources in colleges and universities. The ideological and political teaching resource bank is developed against the backdrop of the teaching paradigm that combines universities and businesses. Learners’ browsing data history is gathered to create a learning interest model for them. A hybrid collaborative filtering recommendation method was devised, and a recommendation engine was established by Taste component, taking into account individualised resource recommendation needs and information entropy weight distribution mode. When compared to previous techniques, the developed customised recommendation method considerably enhances the recommendation quality of instructional resources and reduces MAE by 29% and 34%, respectively.
Highlights
People may quickly get all kinds of resources, thanks to the rapid growth of the mobile Internet, but they face the problem of “information backpacking” and “knowledge overload,” making it more difficult to discover the information they want. e lack of a tailored platform can be compensated for by customised recommendations. e technology gathers user data, creates human portraits using big data, and delivers tailored suggestions. e development and use of a personalized recommendation system have a bright future
People are increasingly demanding tailored learning as modern society progresses. e rapid growth of online learning technology is due to the advent of education informatization [1]
Providing learners with learning resource suggestion services based on their characteristics can reflect individualised learning, assist learners in swiftly finding important materials, and increase the efficiency of online learning [4]
Summary
People were less able to get materials when the Internet was not invented. People may quickly get all kinds of resources, thanks to the rapid growth of the mobile Internet, but they face the problem of “information backpacking” and “knowledge overload,” making it more difficult to discover the information they want. e lack of a tailored platform can be compensated for by customised recommendations. e technology gathers user data, creates human portraits using big data, and delivers tailored suggestions. e development and use of a personalized recommendation system have a bright future. A network-teaching platform may offer a huge number of teaching resources, most users will have access to the same ones. Due to their various interests and activities, users have varying demands for educational resources [3]. Users may uncover prospective interests and hobbies and make suggestions using a variety of recommendation technologies, allowing them to quickly and correctly locate resources suited for their learning requirements among a Mathematical Problems in Engineering vast number of instructional materials and satisfy their wants for tailored services. This article combines it with the project’s instructional resources features, as well as the design process for particular suggestions It is capable of avoiding the sparse matrix issues that the collaborative filtering method encounters.
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